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LOGISTICS PLANNING
The Increased Importance of Logistics
• A Reduction in Economic Regulation
• Recognition by Prominent Non-Logisticians
• Technological Advances
• The Growing Power of Retailers
• Globalization of Trade
Three objectives of logistics strategy:
• Cost reduction (variable costs)
• Capital reduction (investment, fixed costs)
• Service Improvement (may be at odds with
the above two objectives).
VRP Solutions
• Heuristics
– Construction: build a feasible route.
– Improvement: improve a feasible route.
• Not necessarily optimal, but fast.
• Performance depends on problem.
• Worst case performance may be very poor.
• Exact algorithms
– Integer programming.
– Branch and bound.
• Optimal, but usually slow and applicable for small size
problem
• Difficult to include complications.
The VRP is applicable in many practical situations directly related to
 the physical delivery of goods such as
 distribution of petroleum products,
 distribution of industrial gases,
 newspaper deliveries,
 delivery of goods to retail store,
 garbage collection and disposal,
 package pick-up and delivery,
 milk pick-up and delivery, etc.
 the non-movement of goods such as
 picking up of students by school buses,
 routing of salesmen,
 reading of electric meters,
 preventive maintenance inspection tours,
 employee pick-up and drop-off , etc.
APPLICATIONS OF VRP
 A DSS
 Employee Bus Routing
 Commodity Distribution
 In COVERS
 Efficient Heuristic Procedures
 NNH
 MNNH
 MSCWH
 Simulation Features
 Manipulate the System Generated Routes
 Completely User Generated Routes
 COVERS Handles
 Multi-Depot VRP
 Heterogeneous VRP
COVERS- COMPUTERIZED VEHICLE ROUTING SYSTEM
EMPLOYEE PICKUP VEHICLE ROUTING PROBLEM (EPVRP) –
BANGALORE, KARNATAKA, INDIA
 Indian Telephone Industries [ITI] Limited
 Bharat Electronics Limited [BEL]
 Hindustan Machine Tools [HMT]
 Hindustan Aeronautics Limited [HAL]
 Indian Space Research Organization [ISRO]
 National Aeronautical Laboratory [NAL]
 Central Machine Tools of India [CMTI]
 ………
 Nearest Insertion Heuristic (NIH)
 Cheapest Insertion Heuristic (CIH)
 Parallel Version of Clarke & Wright Heuristic (PCWH)
 Sequential Version of Clarke & Wright Heuristic (SCWH)
 Convex Hull Heuristic (CHH)
 Nearest Neighbour Heuristic (NHH)
 Modified NNH (MNNH)
 Modified SCWH 1 (MSCWH-1)
 Modified SCWH 2 (MSCWH-2)
HEURISTIC ALGORITHMS
CASE STUDY : DETAILS OF ROUTES, DISTANCES & SEAT UTILIZATION
Shift Timings #
Commuters
# Pickup
Points
#
Routes
Total Distance
per Trip
(Km.)
Seat
Utilization (%)
A 06.15 – 02.15 PM 3659 303 64 1977.0 89.0
FG 07.30 – 04.15 PM 3999 313 66 2163.0 94.3
AG 08.45 – 05.30 PM 3042 286 53 1808.3 90.0
B 02.15 – 10.15 PM 975 242 30 1056.7 54.0
C 
10.15 – 06.15 AM 40 ---- ---- ---- ----
Total 11715 410
213+
(426)
7005.0 
(14010)
----
 Ignored in our study
 Each Bus Route (Trip) Repeated; Two Trips a day, Once for Pick-up and once for Drop-off.
 Distinct Pick-up Points
COMPARATIVE PERFORMANCE (CASE STUDY) – TOTAL DISTANCE
Procedures Shift – 1
A
Shift – 2
FG
Shift – 3
AG
Shift – 4
B
Total Distance
(Km.)
Savings
(in %)
CPU Time
PC/AT – 486
@ 33 MHz
(Minutes)
Existing
Practice
(Manual)
1977.0 2163.0 1808.3 1056.7 7005.0 ----- ----
NIH 1875.8 2047.7 1734.1 890.3 6547.9 6.5 12
CIH 2155.2 2322.3 1914.2 1020.7 7412.4 - 5.8 52
PCWH 1803.5 2026.1 1761.1 1080.9 6671.6 4.76 19
SCWH 2139.2 2306.6 1889.2 1014.5 7349.5 - 4.9 18
CHH 1903.8 2047.7 1749.2 964.7 6665.4 4.85 55
NNH 1822.9 2063.2 1708.0 900.0 6494.1 7.29 1
MNNH 1817.7 2040.8 1740.7 858.9 6458.1 7.81 1
MSCWH-1 1796.2 2066.4 1687.5 910.2 6460.3 7.78 2
MSCWH-2 1799.4 2047.0 1688.5 908.5 6443.4 8.02 2
(Figures in Table represent travel distance in Km. For Pick-up only)
 Nearest Neighbour Heuristic (NHH)
 Modified NNH (MNNH)
 Modified SCWH-2 (MSCWH-2)
HEURISTIC ALGORITHMS - DSS IMPLEMENTATION

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mathi033ggnnnnnnnnnnnnnnnnnnnnnnnnnn.ppt

  • 2. The Increased Importance of Logistics • A Reduction in Economic Regulation • Recognition by Prominent Non-Logisticians • Technological Advances • The Growing Power of Retailers • Globalization of Trade Three objectives of logistics strategy: • Cost reduction (variable costs) • Capital reduction (investment, fixed costs) • Service Improvement (may be at odds with the above two objectives).
  • 3. VRP Solutions • Heuristics – Construction: build a feasible route. – Improvement: improve a feasible route. • Not necessarily optimal, but fast. • Performance depends on problem. • Worst case performance may be very poor. • Exact algorithms – Integer programming. – Branch and bound. • Optimal, but usually slow and applicable for small size problem • Difficult to include complications.
  • 4. The VRP is applicable in many practical situations directly related to  the physical delivery of goods such as  distribution of petroleum products,  distribution of industrial gases,  newspaper deliveries,  delivery of goods to retail store,  garbage collection and disposal,  package pick-up and delivery,  milk pick-up and delivery, etc.  the non-movement of goods such as  picking up of students by school buses,  routing of salesmen,  reading of electric meters,  preventive maintenance inspection tours,  employee pick-up and drop-off , etc. APPLICATIONS OF VRP
  • 5.  A DSS  Employee Bus Routing  Commodity Distribution  In COVERS  Efficient Heuristic Procedures  NNH  MNNH  MSCWH  Simulation Features  Manipulate the System Generated Routes  Completely User Generated Routes  COVERS Handles  Multi-Depot VRP  Heterogeneous VRP COVERS- COMPUTERIZED VEHICLE ROUTING SYSTEM
  • 6. EMPLOYEE PICKUP VEHICLE ROUTING PROBLEM (EPVRP) – BANGALORE, KARNATAKA, INDIA  Indian Telephone Industries [ITI] Limited  Bharat Electronics Limited [BEL]  Hindustan Machine Tools [HMT]  Hindustan Aeronautics Limited [HAL]  Indian Space Research Organization [ISRO]  National Aeronautical Laboratory [NAL]  Central Machine Tools of India [CMTI]  ………
  • 7.  Nearest Insertion Heuristic (NIH)  Cheapest Insertion Heuristic (CIH)  Parallel Version of Clarke & Wright Heuristic (PCWH)  Sequential Version of Clarke & Wright Heuristic (SCWH)  Convex Hull Heuristic (CHH)  Nearest Neighbour Heuristic (NHH)  Modified NNH (MNNH)  Modified SCWH 1 (MSCWH-1)  Modified SCWH 2 (MSCWH-2) HEURISTIC ALGORITHMS
  • 8. CASE STUDY : DETAILS OF ROUTES, DISTANCES & SEAT UTILIZATION Shift Timings # Commuters # Pickup Points # Routes Total Distance per Trip (Km.) Seat Utilization (%) A 06.15 – 02.15 PM 3659 303 64 1977.0 89.0 FG 07.30 – 04.15 PM 3999 313 66 2163.0 94.3 AG 08.45 – 05.30 PM 3042 286 53 1808.3 90.0 B 02.15 – 10.15 PM 975 242 30 1056.7 54.0 C  10.15 – 06.15 AM 40 ---- ---- ---- ---- Total 11715 410 213+ (426) 7005.0  (14010) ----  Ignored in our study  Each Bus Route (Trip) Repeated; Two Trips a day, Once for Pick-up and once for Drop-off.  Distinct Pick-up Points
  • 9. COMPARATIVE PERFORMANCE (CASE STUDY) – TOTAL DISTANCE Procedures Shift – 1 A Shift – 2 FG Shift – 3 AG Shift – 4 B Total Distance (Km.) Savings (in %) CPU Time PC/AT – 486 @ 33 MHz (Minutes) Existing Practice (Manual) 1977.0 2163.0 1808.3 1056.7 7005.0 ----- ---- NIH 1875.8 2047.7 1734.1 890.3 6547.9 6.5 12 CIH 2155.2 2322.3 1914.2 1020.7 7412.4 - 5.8 52 PCWH 1803.5 2026.1 1761.1 1080.9 6671.6 4.76 19 SCWH 2139.2 2306.6 1889.2 1014.5 7349.5 - 4.9 18 CHH 1903.8 2047.7 1749.2 964.7 6665.4 4.85 55 NNH 1822.9 2063.2 1708.0 900.0 6494.1 7.29 1 MNNH 1817.7 2040.8 1740.7 858.9 6458.1 7.81 1 MSCWH-1 1796.2 2066.4 1687.5 910.2 6460.3 7.78 2 MSCWH-2 1799.4 2047.0 1688.5 908.5 6443.4 8.02 2 (Figures in Table represent travel distance in Km. For Pick-up only)
  • 10.  Nearest Neighbour Heuristic (NHH)  Modified NNH (MNNH)  Modified SCWH-2 (MSCWH-2) HEURISTIC ALGORITHMS - DSS IMPLEMENTATION